{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pycocotools==2.0.0 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.2.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting imgaug==0.2.9\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/17/a9/36de8c0e1ffb2d86f871cac60e5caa910cbbdb5f4741df5ef856c47f4445/imgaug-0.2.9-py2.py3-none-any.whl (753kB)\n",
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      "Collecting numpy>=1.15.0 (from imgaug==0.2.9)\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/e5/e6/c3fdc53aed9fa19d6ff3abf97dfad768ae3afce1b7431f7500000816bda5/numpy-1.17.2-cp36-cp36m-manylinux1_x86_64.whl (20.4MB)\n",
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      "\u001b[?25hRequirement already satisfied: imageio in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from imgaug==0.2.9)\n",
      "Requirement already satisfied: Pillow in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from imgaug==0.2.9)\n",
      "Requirement already satisfied: opencv-python in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from imgaug==0.2.9)\n",
      "Requirement already satisfied: six in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from imgaug==0.2.9)\n",
      "Requirement already satisfied: matplotlib in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from imgaug==0.2.9)\n",
      "Collecting Shapely (from imgaug==0.2.9)\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/38/b6/b53f19062afd49bb5abd049aeed36f13bf8d57ef8f3fa07a5203531a0252/Shapely-1.6.4.post2-cp36-cp36m-manylinux1_x86_64.whl (1.5MB)\n",
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      "\u001b[?25hRequirement already satisfied: PyWavelets>=0.4.0 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from scikit-image>=0.11.0->imgaug==0.2.9)\n",
      "Requirement already satisfied: networkx>=1.8 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from scikit-image>=0.11.0->imgaug==0.2.9)\n",
      "Requirement already satisfied: pytz in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->imgaug==0.2.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->imgaug==0.2.9)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->imgaug==0.2.9)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->imgaug==0.2.9)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->imgaug==0.2.9)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from networkx>=1.8->scikit-image>=0.11.0->imgaug==0.2.9)\n",
      "Requirement already satisfied: setuptools in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->imgaug==0.2.9)\n",
      "Installing collected packages: numpy, Shapely, imgaug\n",
      "  Found existing installation: numpy 1.14.5\n",
      "    Uninstalling numpy-1.14.5:\n",
      "      Successfully uninstalled numpy-1.14.5\n",
      "  Found existing installation: imgaug 0.2.6\n",
      "    Uninstalling imgaug-0.2.6:\n",
      "      Successfully uninstalled imgaug-0.2.6\n",
      "Successfully installed Shapely-1.6.4.post2 imgaug-0.2.9 numpy-1.17.2\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.2.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Uninstalling numpy-1.17.2:\n",
      "  Successfully uninstalled numpy-1.17.2\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.2.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting numpy==1.15.4\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/ff/7f/9d804d2348471c67a7d8b5f84f9bc59fd1cefa148986f2b74552f8573555/numpy-1.15.4-cp36-cp36m-manylinux1_x86_64.whl (13.9MB)\n",
      "\u001b[K    100% |████████████████████████████████| 13.9MB 95.1MB/s ta 0:00:011| 256kB 87.2MB/s eta 0:00:01         | 5.1MB 89.7MB/s eta 0:00:01██████▌          | 9.3MB 88.2MB/s eta 0:00:01��████████▎| 13.6MB 95.6MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: numpy\n",
      "Successfully installed numpy-1.15.4\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.2.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: seaborn in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages\n",
      "Requirement already satisfied: numpy in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from seaborn)\n",
      "Requirement already satisfied: scipy in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from seaborn)\n",
      "Requirement already satisfied: matplotlib in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from seaborn)\n",
      "Requirement already satisfied: pandas in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from seaborn)\n",
      "Requirement already satisfied: pytz in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: six>=1.10 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: setuptools in /home/ma-user/anaconda3/envs/TensorFlow-1.8/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->seaborn)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 19.2.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install pycocotools==2.0.0\n",
    "!pip install imgaug==0.2.9\n",
    "!pip uninstall numpy -y\n",
    "!pip install numpy==1.15.4\n",
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully download file ai-awe-001/wabao/data.zip from OBS to local ./data.zip\n"
     ]
    }
   ],
   "source": [
    "from modelarts.session import Session\n",
    "session = Session()\n",
    "\n",
    "bucket_path=\"ai-awe-001/wabao/data.zip\"\n",
    "\n",
    "    \n",
    "session.download_data(bucket_path=bucket_path,\n",
    "                      path='./data.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*\t\t   output\t\t\t\t\t   test3.jpg\n",
      "data.zip\t   resnet50_weights_tf_dim_ordering_tf_kernels.h5  test.jpg\n",
      "model1\t\t   test\t\t\t\t\t\t   training\n",
      "monkey_labels.txt  test1.jpg\t\t\t\t\t   validation\n",
      "monky.ipynb\t   test2.jpg\n",
      "Archive:  data.zip\n",
      "replace validation/n0/n000.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n",
      "*\t\t   output\t\t\t\t\t   test3.jpg\n",
      "data.zip\t   resnet50_weights_tf_dim_ordering_tf_kernels.h5  test.jpg\n",
      "model1\t\t   test\t\t\t\t\t\t   training\n",
      "monkey_labels.txt  test1.jpg\t\t\t\t\t   validation\n",
      "monky.ipynb\t   test2.jpg\n"
     ]
    }
   ],
   "source": [
    "!ls\n",
    "!unzip data.zip\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import glob\n",
    "import h5py\n",
    "import shutil\n",
    "import imgaug as aug\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import imgaug.augmenters as iaa\n",
    "from os import listdir, makedirs, getcwd, remove\n",
    "from os.path import isfile, join, abspath, exists, isdir, expanduser\n",
    "from pathlib import Path\n",
    "from skimage.io import imread\n",
    "from skimage.transform import resize\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten\n",
    "from keras.optimizers import Adam, SGD, RMSprop\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from keras import backend as K\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Label             Latin Name                Common Name  Train Images  \\\n",
      "0    n0      alouatta_palliata             mantled_howler           131   \n",
      "1    n1     erythrocebus_patas               patas_monkey           139   \n",
      "2    n2         cacajao_calvus                bald_uakari           137   \n",
      "3    n3         macaca_fuscata           japanese_macaque           152   \n",
      "4    n4        cebuella_pygmea             pygmy_marmoset           131   \n",
      "5    n5        cebus_capucinus      white_headed_capuchin           141   \n",
      "6    n6        mico_argentatus           silvery_marmoset           132   \n",
      "7    n7       saimiri_sciureus     common_squirrel_monkey           142   \n",
      "8    n8        aotus_nigriceps  black_headed_night_monkey           133   \n",
      "9    n9  trachypithecus_johnii             nilgiri_langur           132   \n",
      "\n",
      "   Validation Images  \n",
      "0                 26  \n",
      "1                 28  \n",
      "2                 27  \n",
      "3                 30  \n",
      "4                 26  \n",
      "5                 28  \n",
      "6                 26  \n",
      "7                 28  \n",
      "8                 27  \n",
      "9                 26  \n",
      "{0: 'mantled_howler', 1: 'patas_monkey', 2: 'bald_uakari', 3: 'japanese_macaque', 4: 'pygmy_marmoset', 5: 'white_headed_capuchin', 6: 'silvery_marmoset', 7: 'common_squirrel_monkey', 8: 'black_headed_night_monkey', 9: 'nilgiri_langur'}\n",
      "Number of traininng samples:  1096\n",
      "Number of validation samples:  272\n",
      "\n",
      "                    image  label\n",
      "0  training/n7/n7151.jpg      7\n",
      "1  training/n3/n3067.jpg      3\n",
      "2  training/n5/n5154.jpg      5\n",
      "3  training/n0/n0116.jpg      0\n",
      "4  training/n3/n3117.jpg      3 \n",
      "\n",
      "=================================================================\n",
      "\n",
      "\n",
      "                     image  label\n",
      "0  validation/n2/n201.jpg      2\n",
      "1  validation/n4/n406.jpg      4\n",
      "2  validation/n2/n215.jpg      2\n",
      "3  validation/n7/n709.jpg      7\n",
      "4  validation/n3/n313.jpg      3\n"
     ]
    }
   ],
   "source": [
    "training_data = Path('./training/') \n",
    "validation_data = Path('./validation/') \n",
    "labels_path = Path('./monkey_labels.txt')\n",
    "\n",
    "labels_info = []\n",
    "\n",
    "lines = labels_path.read_text().strip().splitlines()[1:]\n",
    "for line in lines:\n",
    "    line = line.split(',')\n",
    "    line = [x.strip(' \\n\\t\\r') for x in line]\n",
    "    line[3], line[4] = int(line[3]), int(line[4])\n",
    "    line = tuple(line)\n",
    "    labels_info.append(line)\n",
    "    \n",
    "labels_info = pd.DataFrame(labels_info, columns=['Label', 'Latin Name', 'Common Name', \n",
    "                                                 'Train Images', 'Validation Images'], index=None)\n",
    "print(labels_info.head(10))\n",
    "\n",
    "labels_dict= {'n0':0, 'n1':1, 'n2':2, 'n3':3, 'n4':4, 'n5':5, 'n6':6, 'n7':7, 'n8':8, 'n9':9}\n",
    "\n",
    "names_dict = dict(zip(labels_dict.values(), labels_info[\"Common Name\"]))\n",
    "print(names_dict)\n",
    "\n",
    "train_df = []\n",
    "for folder in os.listdir(training_data):\n",
    "    imgs_path = training_data / folder\n",
    "    \n",
    "    imgs = sorted(imgs_path.glob('*.jpg'))\n",
    "    \n",
    "    for img_name in imgs:\n",
    "        train_df.append((str(img_name), labels_dict[folder]))\n",
    "\n",
    "\n",
    "train_df = pd.DataFrame(train_df, columns=['image', 'label'], index=None)\n",
    "train_df = train_df.sample(frac=1.).reset_index(drop=True)\n",
    "\n",
    "valid_df = []\n",
    "for folder in os.listdir(validation_data):\n",
    "    imgs_path = validation_data / folder\n",
    "    imgs = sorted(imgs_path.glob('*.jpg'))\n",
    "    for img_name in imgs:\n",
    "        valid_df.append((str(img_name), labels_dict[folder]))\n",
    "\n",
    "        \n",
    "valid_df = pd.DataFrame(valid_df, columns=['image', 'label'], index=None)\n",
    "\n",
    "valid_df = valid_df.sample(frac=1.).reset_index(drop=True)\n",
    "\n",
    "print(\"Number of traininng samples: \", len(train_df))\n",
    "print(\"Number of validation samples: \", len(valid_df))\n",
    "\n",
    "print(\"\\n\",train_df.head(), \"\\n\")\n",
    "print(\"=================================================================\\n\")\n",
    "print(\"\\n\", valid_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
      "102858752/102853048 [==============================] - 184s 2us/step\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 55, 55, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 55, 55, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 55, 55, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 55, 55, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 55, 55, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 55, 55, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 55, 55, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 55, 55, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 55, 55, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 55, 55, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 55, 55, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 55, 55, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 55, 55, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 55, 55, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 55, 55, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 55, 55, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 55, 55, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "drop2 (Dropout)                 (None, 2048)         0           flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "fc3 (Dense)                     (None, 10)           20490       drop2[0][0]                      \n",
      "==================================================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 20,490\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "img_rows, img_cols, img_channels = 224,224,3\n",
    "\n",
    "# batch size for training  \n",
    "batch_size=8\n",
    "\n",
    "# total number of classes in the dataset\n",
    "nb_classes=10\n",
    "\n",
    "def get_base_model():\n",
    "    base_model = ResNet50(input_shape=(img_rows, img_cols, img_channels), weights='imagenet', include_top=True)\n",
    "    return base_model\n",
    "base_model = get_base_model()\n",
    "base_model_output = base_model.layers[-2].output\n",
    "x = Dropout(0.7,name='drop2')(base_model_output)\n",
    "output = Dense(10, activation='softmax', name='fc3')(x)\n",
    "model = Model(base_model.input, output)\n",
    "for layer in base_model.layers[:-1]:\n",
    "    layer.trainable=False\n",
    "\n",
    "optimizer = RMSprop(0.001)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq = iaa.OneOf([\n",
    "    iaa.Fliplr(), # horizontal flips\n",
    "    iaa.Affine(rotate=20), # roatation\n",
    "    iaa.Multiply((1.2, 1.5))]) #random brightness\n",
    "\n",
    "def data_generator(data, batch_size, is_validation_data=False):\n",
    "    n = len(data)\n",
    "    nb_batches = int(np.ceil(n/batch_size))\n",
    "\n",
    "    indices = np.arange(n)\n",
    "    batch_data = np.zeros((batch_size, img_rows, img_cols, img_channels), dtype=np.float32)\n",
    "    batch_labels = np.zeros((batch_size, nb_classes), dtype=np.float32)\n",
    "    \n",
    "    while True:\n",
    "        if not is_validation_data:\n",
    "            np.random.shuffle(indices)\n",
    "            \n",
    "        for i in range(nb_batches):\n",
    "            next_batch_indices = indices[i*batch_size:(i+1)*batch_size]\n",
    "            for j, idx in enumerate(next_batch_indices):\n",
    "                img = cv2.imread(data.iloc[idx][\"image\"])\n",
    "                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "                label = data.iloc[idx][\"label\"]\n",
    "                \n",
    "                if not is_validation_data:\n",
    "                    img = seq.augment_image(img)\n",
    "                \n",
    "                img = cv2.resize(img, (img_rows, img_cols)).astype(np.float32)\n",
    "                batch_data[j] = img\n",
    "                batch_labels[j] = to_categorical(label,num_classes=nb_classes)\n",
    "            \n",
    "            batch_data = preprocess_input(batch_data)\n",
    "            yield batch_data, batch_labels\n",
    "\n",
    "#training data generator \n",
    "train_data_gen = data_generator(train_df, batch_size)\n",
    "\n",
    "# validation data generator \n",
    "valid_data_gen = data_generator(valid_df, batch_size, is_validation_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='val_loss', patience=3, verbose=1, mode='auto')\n",
    "\n",
    "# checkpoint to save model\n",
    "chkpt = ModelCheckpoint(filepath=\"model1\", save_best_only=True)\n",
    "\n",
    "# number of training and validation steps for training and validation\n",
    "nb_train_steps = int(np.ceil(len(train_df)/batch_size))\n",
    "nb_valid_steps = int(np.ceil(len(valid_df)/batch_size))\n",
    "\n",
    "# number of epochs \n",
    "# nb_epochs=30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "137/137 [==============================] - 30s 222ms/step - loss: 0.5858 - acc: 0.8403 - val_loss: 0.5929 - val_acc: 0.8934\n",
      "Epoch 2/10\n",
      "137/137 [==============================] - 30s 219ms/step - loss: 0.5219 - acc: 0.8522 - val_loss: 0.4598 - val_acc: 0.9301\n",
      "Epoch 3/10\n",
      "137/137 [==============================] - 28s 201ms/step - loss: 0.5744 - acc: 0.8485 - val_loss: 0.7110 - val_acc: 0.8934\n",
      "Epoch 4/10\n",
      "137/137 [==============================] - 27s 198ms/step - loss: 0.4583 - acc: 0.8887 - val_loss: 0.4894 - val_acc: 0.9301\n",
      "Epoch 5/10\n",
      "137/137 [==============================] - 29s 208ms/step - loss: 0.5641 - acc: 0.8568 - val_loss: 0.2764 - val_acc: 0.9449\n",
      "Epoch 6/10\n",
      "137/137 [==============================] - 29s 212ms/step - loss: 0.5488 - acc: 0.8704 - val_loss: 0.4717 - val_acc: 0.9044\n",
      "Epoch 7/10\n",
      "137/137 [==============================] - 29s 212ms/step - loss: 0.5255 - acc: 0.8777 - val_loss: 0.3660 - val_acc: 0.9301\n",
      "Epoch 8/10\n",
      "137/137 [==============================] - 26s 192ms/step - loss: 0.5325 - acc: 0.8723 - val_loss: 0.8821 - val_acc: 0.8787\n",
      "Epoch 00008: early stopping\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_data_gen, \n",
    "                              epochs=10, \n",
    "                              steps_per_epoch=nb_train_steps, \n",
    "                              validation_data=valid_data_gen, \n",
    "                              validation_steps=nb_valid_steps,\n",
    "                              callbacks=[es,chkpt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final validation accuracy: 90.81%\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "train_acc = history.history['acc']\n",
    "valid_acc = history.history['val_acc']\n",
    "\n",
    "train_loss = history.history['loss']\n",
    "valid_loss = history.history['val_loss']\n",
    "\n",
    "xvalues = np.arange(len(train_acc))\n",
    "\n",
    "f,ax = plt.subplots(1,2, figsize=(10,5))\n",
    "ax[0].plot(xvalues, train_loss)\n",
    "ax[0].plot(xvalues, valid_loss)\n",
    "ax[0].set_title(\"Loss curve\")\n",
    "ax[0].set_xlabel(\"Epoch\")\n",
    "ax[0].set_ylabel(\"loss\")\n",
    "ax[0].legend(['train', 'validation'])\n",
    "\n",
    "ax[1].plot(xvalues, train_acc)\n",
    "ax[1].plot(xvalues, valid_acc)\n",
    "ax[1].set_title(\"Accuracy\")\n",
    "ax[1].set_xlabel(\"Epoch\")\n",
    "ax[1].set_ylabel(\"accuracy\")\n",
    "ax[1].legend(['train', 'validation'])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "valid_loss, valid_acc = model.evaluate_generator(valid_data_gen, steps=nb_valid_steps)\n",
    "print(f\"Final validation accuracy: {valid_acc*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 55, 55, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 55, 55, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 55, 55, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 55, 55, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 55, 55, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 55, 55, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 55, 55, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 55, 55, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 55, 55, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 55, 55, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 55, 55, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 55, 55, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 55, 55, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 55, 55, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 55, 55, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 55, 55, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 55, 55, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "drop2 (Dropout)                 (None, 2048)         0           flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "fc3 (Dense)                     (None, 10)           20490       drop2[0][0]                      \n",
      "==================================================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 20,490\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "outputs = [layer.output for layer in model.layers[1:]]\n",
    "\n",
    "vis_model = Model(model.input, outputs)\n",
    "\n",
    "vis_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layers going to be used for visualization: \n",
      "['conv1_pad', 'conv1', 'bn_conv1', 'activation_1', 'max_pooling2d_1', 'res2a_branch2a', 'bn2a_branch2a', 'activation_2', 'res2a_branch2b', 'bn2a_branch2b', 'activation_3', 'res2a_branch2c', 'res2a_branch1', 'bn2a_branch2c', 'bn2a_branch1', 'add_1', 'activation_4', 'res2b_branch2a', 'bn2b_branch2a', 'activation_5', 'res2b_branch2b', 'bn2b_branch2b', 'activation_6', 'res2b_branch2c', 'bn2b_branch2c', 'add_2', 'activation_7', 'res2c_branch2a', 'bn2c_branch2a', 'activation_8', 'res2c_branch2b', 'bn2c_branch2b', 'activation_9', 'res2c_branch2c', 'bn2c_branch2c', 'add_3', 'activation_10', 'res3a_branch2a', 'bn3a_branch2a', 'activation_11', 'res3a_branch2b', 'bn3a_branch2b', 'activation_12', 'res3a_branch2c', 'res3a_branch1', 'bn3a_branch2c', 'bn3a_branch1', 'add_4', 'activation_13', 'res3b_branch2a', 'bn3b_branch2a', 'activation_14', 'res3b_branch2b', 'bn3b_branch2b', 'activation_15', 'res3b_branch2c', 'bn3b_branch2c', 'add_5', 'activation_16', 'res3c_branch2a', 'bn3c_branch2a', 'activation_17', 'res3c_branch2b', 'bn3c_branch2b', 'activation_18', 'res3c_branch2c', 'bn3c_branch2c', 'add_6', 'activation_19', 'res3d_branch2a', 'bn3d_branch2a', 'activation_20', 'res3d_branch2b', 'bn3d_branch2b', 'activation_21', 'res3d_branch2c', 'bn3d_branch2c', 'add_7', 'activation_22', 'res4a_branch2a', 'bn4a_branch2a', 'activation_23', 'res4a_branch2b', 'bn4a_branch2b', 'activation_24', 'res4a_branch2c', 'res4a_branch1', 'bn4a_branch2c', 'bn4a_branch1', 'add_8', 'activation_25', 'res4b_branch2a', 'bn4b_branch2a', 'activation_26', 'res4b_branch2b', 'bn4b_branch2b', 'activation_27', 'res4b_branch2c', 'bn4b_branch2c', 'add_9', 'activation_28', 'res4c_branch2a', 'bn4c_branch2a', 'activation_29', 'res4c_branch2b', 'bn4c_branch2b', 'activation_30', 'res4c_branch2c', 'bn4c_branch2c', 'add_10', 'activation_31', 'res4d_branch2a', 'bn4d_branch2a', 'activation_32', 'res4d_branch2b', 'bn4d_branch2b', 'activation_33', 'res4d_branch2c', 'bn4d_branch2c', 'add_11', 'activation_34', 'res4e_branch2a', 'bn4e_branch2a', 'activation_35', 'res4e_branch2b', 'bn4e_branch2b', 'activation_36', 'res4e_branch2c', 'bn4e_branch2c', 'add_12', 'activation_37', 'res4f_branch2a', 'bn4f_branch2a', 'activation_38', 'res4f_branch2b', 'bn4f_branch2b', 'activation_39', 'res4f_branch2c', 'bn4f_branch2c', 'add_13', 'activation_40', 'res5a_branch2a', 'bn5a_branch2a', 'activation_41', 'res5a_branch2b', 'bn5a_branch2b', 'activation_42', 'res5a_branch2c', 'res5a_branch1', 'bn5a_branch2c', 'bn5a_branch1', 'add_14', 'activation_43', 'res5b_branch2a', 'bn5b_branch2a', 'activation_44', 'res5b_branch2b', 'bn5b_branch2b', 'activation_45', 'res5b_branch2c', 'bn5b_branch2c', 'add_15', 'activation_46', 'res5c_branch2a', 'bn5c_branch2a', 'activation_47', 'res5c_branch2b', 'bn5c_branch2b', 'activation_48', 'res5c_branch2c', 'bn5c_branch2c', 'add_16', 'activation_49', 'avg_pool', 'flatten_1', 'drop2', 'fc3']\n"
     ]
    }
   ],
   "source": [
    "layer_names = []\n",
    "for layer in outputs:\n",
    "    layer_names.append(layer.name.split(\"/\")[0])\n",
    "\n",
    "    \n",
    "print(\"Layers going to be used for visualization: \")\n",
    "print(layer_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OUTPUT = 'output'\n",
    "\n",
    "# if not os.path.exists(OUTPUT):\n",
    "#     os.mkdir(OUTPUT)\n",
    "\n",
    "# model.save(os.path.join(OUTPUT, 'model.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_CAM(processed_image, predicted_label):\n",
    "#     \"\"\"\n",
    "#     This function is used to generate a heatmap for a sample image prediction.\n",
    "    \n",
    "#     Args:\n",
    "#         processed_image: any sample image that has been pre-processed using the \n",
    "#                        `preprocess_input()`method of a keras model\n",
    "#         predicted_label: label that has been predicted by the network for this image\n",
    "    \n",
    "#     Returns:\n",
    "#         heatmap: heatmap generated over the last convolution layer output \n",
    "#     \"\"\"\n",
    "#     # we want the activations for the predicted label\n",
    "#     predicted_output = model.output[:, predicted_label]\n",
    "    \n",
    "#     # choose the last conv layer in your model\n",
    "#     last_conv_layer = model.get_layer('res5c_branch2c')\n",
    "    \n",
    "#     # get the gradients wrt to the last conv layer\n",
    "#     grads = K.gradients(predicted_output, last_conv_layer.output)[0]\n",
    "    \n",
    "#     # take mean gradient per feature map\n",
    "#     grads = K.mean(grads, axis=(0,1,2))\n",
    "    \n",
    "#     # Define a function that generates the values for the output and gradients\n",
    "#     evaluation_function = K.function([model.input], [grads, last_conv_layer.output[0]])\n",
    "    \n",
    "#     # get the values\n",
    "#     grads_values, conv_ouput_values = evaluation_function([processed_image])\n",
    "    \n",
    "#     # iterate over each feature map in yout conv output and multiply\n",
    "#     # the gradient values with the conv output values. This gives an \n",
    "#     # indication of \"how important a feature is\"\n",
    "#     for i in range(2048): # we have 512 features in our last conv layer\n",
    "#         conv_ouput_values[:,:,i] *= grads_values[i]\n",
    "    \n",
    "#     # create a heatmap\n",
    "#     heatmap = np.mean(conv_ouput_values, axis=-1)\n",
    "    \n",
    "#     # remove negative values\n",
    "#     heatmap = np.maximum(heatmap, 0)\n",
    "    \n",
    "#     # normalize\n",
    "#     heatmap /= heatmap.max()\n",
    "    \n",
    "#     return heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True label: 6 \n",
      " Predicted label: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_random_sample(idx):\n",
    "    # select the sample and read the corresponding image and label\n",
    "    sample_image = cv2.imread(valid_df.iloc[idx]['image'])\n",
    "    sample_image = cv2.cvtColor(sample_image, cv2.COLOR_BGR2RGB)\n",
    "    sample_image = cv2.resize(sample_image, (img_rows, img_cols))\n",
    "    sample_label = valid_df.iloc[idx][\"label\"]\n",
    "    \n",
    "    # pre-process the image\n",
    "    sample_image_processed = np.expand_dims(sample_image, axis=0)\n",
    "    sample_image_processed = preprocess_input(sample_image_processed)\n",
    "    \n",
    "    # generate activation maps from the intermediate layers using the visualization model\n",
    "    activations = vis_model.predict(sample_image_processed)\n",
    "    \n",
    "    # get the label predicted by our original model\n",
    "    pred_label = np.argmax(model.predict(sample_image_processed), axis=-1)[0]\n",
    "\n",
    "    plt.imshow(sample_image)\n",
    "    print(f\"True label: {sample_label} \\n Predicted label: {pred_label}\")\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    return activations\n",
    "\n",
    "activations= show_random_sample(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True label: 4 \n",
      " Predicted label: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import randint\n",
    "n = randint(1,300)\n",
    "activations= show_random_sample(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow-1.8",
   "language": "python",
   "name": "tensorflow-1.8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
